Akustik temelli araç trafik yoğunluğu kestirimi
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Date
2019-04-02
Authors
Öztürk, Fikret
Hocaoğlu, Ali Köksal
Journal Title
Journal ISSN
Volume Title
Publisher
Bursa Uludağ Üniversitesi
Abstract
Bu çalışmada, araçların oluşturduğu akustik gürültü sinyalinden trafik yoğunluğunun kestirimi yapılmıştır. Akustik gürültü sinyali, motor, hava türbülans, tekerlek, egzoz ve korna gürültü bileşenlerinden oluşmaktadır. Trafik yoğunluk durumuna göre bu bileşenlerin bulunma ağırlıkları değişmektedir. Örneğin trafiğin yoğun olduğu zaman motor ve korna gürültüsü yoğun, trafiğin akışkan olduğu zaman hava türbülansı ve tekerlek gürültüsü daha yoğundur. Akustik gürültü sinyalindeki bu farklılıktan faydalanılarak trafik yoğunluğu yoğun, orta ve serbest akış olmak üzere üç sınıfa ayrılmıştır. Önerilen yöntem Mel-frekans kepstrum katsayıları (MFCC, Mel-Frequency Cepstral Coefficients) özniteliklerini ve sınıflandırıcı olarak k-en yakın komşu yöntemini kullanmaktadır. E5 karayolunda özgün bir veri seti üretilmiş ve önerilen yöntem bu veri seti kullanılarak test edilmiştir. MFCC özniteliklerine ilişkin parametrelerin trafik yoğunluğu tespitine etkisi incelenmiştir ve en önemli iki parametrenin kepstrum katsayı sayısı ve pencere süresi olduğu görülmüştür. Hava durumunu dikkate alarak sınıflandırıcı eğitmenin performansı iyileştirdiği gösterilmiştir. Bu iyileştirmenin sebebi irdelenmiş ve iki boyutlu öznitelik uzayında gösterilmiştir. E5 karayolunda trafik yoğunluğu yağışlı havalarda %90, yağış olmayan durumlarda ise %82 doğrulukla tespit edilmiştir.
In this study, traffic density is estimated using acoustic noise signals formed by the land vehicles. The acoustic noise signals formed by the vehicles consist of engine noise, air turbulence, the noise of the wheels touching the floor, exhaust noise and the horn noise. The contributions of these different types of noise change according to the traffic density. For example, engine noise and horn noise are dense when the traffic is busy and when the traffic is free-flow, air turbulence and wheel noise are more dense. By taking advantage of this change in the acoustic noise signal, the traffic density is categorized into three classes; busy, normal and free-flow. The proposed method use Mel-Frequency Cepstral Coefficients (MFCC) to extract features and the k-Nearest Neighbor Rule to classify. A data set was formed on E5 roadway and it was used to evaluate the proposed method. The effect of MFCC attributes on the traffic density estimation was investigated and the number of cepstral coefficients and the duration of windows are found to be the most important ones. It is shown that the performance of the traffic density estimation is increased if the weather conditions are considered when training the classifiers. The reason behind this improvement is investigated and shown on a two dimensional feature space. The traffic density in the E5 roadway is determined by %90 and %82 accuracies when raining and not raining, respectively.
In this study, traffic density is estimated using acoustic noise signals formed by the land vehicles. The acoustic noise signals formed by the vehicles consist of engine noise, air turbulence, the noise of the wheels touching the floor, exhaust noise and the horn noise. The contributions of these different types of noise change according to the traffic density. For example, engine noise and horn noise are dense when the traffic is busy and when the traffic is free-flow, air turbulence and wheel noise are more dense. By taking advantage of this change in the acoustic noise signal, the traffic density is categorized into three classes; busy, normal and free-flow. The proposed method use Mel-Frequency Cepstral Coefficients (MFCC) to extract features and the k-Nearest Neighbor Rule to classify. A data set was formed on E5 roadway and it was used to evaluate the proposed method. The effect of MFCC attributes on the traffic density estimation was investigated and the number of cepstral coefficients and the duration of windows are found to be the most important ones. It is shown that the performance of the traffic density estimation is increased if the weather conditions are considered when training the classifiers. The reason behind this improvement is investigated and shown on a two dimensional feature space. The traffic density in the E5 roadway is determined by %90 and %82 accuracies when raining and not raining, respectively.
Description
Keywords
Trafik yoğunluk kestirimi, Karayolu araçları, Akustik sinyal işleme, Örüntü tanıma, Traffic density estimation, Land vehicles, Acoustic signal processing, Pattern recognition
Citation
Öztürk, F. ve Hocaoğlu, A. K. (2019). "Akustik temelli araç trafik yoğunluğu kestirimi". Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 24(1), 429-440.